Update app.py

app.py

@@ -225,16 +225,16 @@ def simple_lstm_predict(ticker, n_days=5):
 # 建立 Dash 應用程式
 app = dash.Dash(__name__, suppress_callback_exceptions=True)
 
-# 新聞預測器初始化
-predictor = None
-try:
-    if BertPredictor:
-        print("正在初始化新聞情緒分析模型...")
-        predictor = BertPredictor(max_news_per_keyword=5)
-        print("新聞情緒分析模型初始化成功。")
-except Exception as e:
-    print(f"錯誤：新聞情緒分析模型初始化失敗 - {e}")
-    predictor = None
+# 註解掉新聞預測器初始化
+# predictor = None
+# try:
+#     if BertPredictor:
+#         print("正在初始化新聞情緒分析模型...")
+#         predictor = BertPredictor(max_news_per_keyword=5)
+#         print("新聞情緒分析模型初始化成功。")
+# except Exception as e:
+#     print(f"錯誤：新聞情緒分析模型初始化失敗 - {e}")
+#     predictor = None
 
 
 # --- 頁面內容定義 ---
@@ -257,19 +257,20 @@ homepage_layout = html.Div([
         html.Div([dcc.Graph(id='taiex-prediction-chart')], style={'margin-top': '20px'})
     ], style={'background': 'linear-gradient(135deg, #667eea 0%, #764ba2 100%)','padding': '25px','border-radius': '15px','box-shadow': '0 8px 25px rgba(0,0,0,0.15)','color': 'white','margin-bottom': '40px'}),
     
-    html.Div([
-        html.H3("📰 市場情緒與新聞分析", style={'color': '#E74C3C', 'margin-bottom': '20px'}),
-        html.Div([
-            html.Div([
-                html.H4("市場情緒指標", style={'color': '#8E44AD'}),
-                html.Div(id='sentiment-gauge')
-            ], style={'width': '48%', 'display': 'inline-block'}),
-            html.Div([
-                html.H4("關鍵新聞摘要", style={'color': '#27AE60'}),
-                html.Div(id='news-summary', style={'background': '#f8f9fa','padding': '15px','border-radius': '8px','max-height': '200px','overflow-y': 'auto'})
-            ], style={'width': '48%', 'display': 'inline-block', 'margin-left': '4%'})
-        ])
-    ], style={'margin-top': '30px','padding': '20px','background': 'white','border-radius': '10px','box-shadow': '0 2px 10px rgba(0,0,0,0.1)'}),
+    # 註解掉情緒分析區塊
+    # html.Div([
+    #     html.H3("📰 市場情緒與新聞分析", style={'color': '#E74C3C', 'margin-bottom': '20px'}),
+    #     html.Div([
+    #         html.Div([
+    #             html.H4("市場情緒指標", style={'color': '#8E44AD'}),
+    #             html.Div(id='sentiment-gauge')
+    #         ], style={'width': '48%', 'display': 'inline-block'}),
+    #         html.Div([
+    #             html.H4("關鍵新聞摘要", style={'color': '#27AE60'}),
+    #             html.Div(id='news-summary', style={'background': '#f8f9fa','padding': '15px','border-radius': '8px','max-height': '200px','overflow-y': 'auto'})
+    #         ], style={'width': '48%', 'display': 'inline-block', 'margin-left': '4%'})
+    #     ])
+    # ], style={'margin-top': '30px','padding': '20px','background': 'white','border-radius': '10px','box-shadow': '0 2px 10px rgba(0,0,0,0.1)'}),
 
     html.Div([
         html.H3("景氣燈號與 PMI 分析"),
@@ -420,67 +421,66 @@ def update_taiex_prediction(n_days):
     
     return result_text, fig
 
-
-@app.callback(
-    dash.dependencies.Output('sentiment-gauge', 'children'),
-    dash.dependencies.Output('news-summary', 'children')
-)
-def update_sentiment_analysis():
-    """更新新聞情緒分析"""
-    if not predictor:
-        return html.Div("新聞情緒分析模型未初始化。"), html.Div("請檢查 'Bert_predict.py' 檔案是否存在。")
-
-    try:
-        sentiment_score, news_list = predictor.get_sentiment_score()
-        
-    except Exception as e:
-        sentiment_score = None
-        news_list = []
-        print(f"情緒分析獲取失敗: {e}")
-    
-    # 1. 建立儀表板 (Gauge)
-    if sentiment_score is not None:
-        gauge_fig = go.Figure(go.Indicator(
-            mode="gauge+number",
-            value=sentiment_score,
-            title={'text': "市場情緒分數 (0-100)"},
-            domain={'x': [0, 1], 'y': [0, 1]},
-            gauge={
-                'axis': {'range': [0, 100]},
-                'bar': {'color': "#667eea"},
-                'steps': [
-                    {'range': [0, 35], 'color': "rgba(217, 83, 79, 0.2)"},
-                    {'range': [35, 65], 'color': "rgba(240, 173, 78, 0.2)"},
-                    {'range': [65, 100], 'color': "rgba(92, 184, 92, 0.2)"}
-                ],
-            }
-        ))
-        gauge_fig.update_layout(height=200, margin=dict(l=30, r=30, t=50, b=20))
-        gauge_content = dcc.Graph(figure=gauge_fig)
-    else:
-        # 處理無法計算分數的情況
-        error_fig = go.Figure().add_annotation(text="今日尚無情緒分數", showarrow=False)
-        error_fig.update_layout(height=200)
-        gauge_content = dcc.Graph(figure=error_fig)
-
-
-    # 2. 從 predictor 獲取分數最高的3則新聞
-    top_news_list = predictor.get_news()
-
-    # 3. 建立新聞摘要元件
-    if top_news_list: # 如果列表不為空
-        news_content = html.Div([
-            html.P(f"• {news}", style={
-                'margin': '8px 0',
-                'padding-left': '5px',
-                'font-size': '14px',
-                'border-left': '3px solid #E74C3C'
-            }) for news in top_news_list
-        ])
-    else:
-        news_content = html.Div("今日尚無重大新聞摘要。")
-    
-    return gauge_content, news_content
+# 註解掉情緒分析回調函數
+# @app.callback(
+#     dash.dependencies.Output('sentiment-gauge', 'children'),
+#     dash.dependencies.Output('news-summary', 'children')
+# )
+# def update_sentiment_analysis():
+#     """更新新聞情緒分析"""
+#     if not predictor:
+#         return html.Div("新聞情緒分析模型未初始化。"), html.Div("請檢查 'Bert_predict.py' 檔案是否存在。")
+
+#     try:
+#         sentiment_score, news_list = predictor.get_sentiment_score()
+#     except Exception as e:
+#         sentiment_score = None
+#         news_list = []
+#         print(f"情緒分析獲取失敗: {e}")
+    
+#     # 1. 建立儀表板 (Gauge)
+#     if sentiment_score is not None:
+#         gauge_fig = go.Figure(go.Indicator(
+#             mode="gauge+number",
+#             value=sentiment_score,
+#             title={'text': "市場情緒分數 (0-100)"},
+#             domain={'x': [0, 1], 'y': [0, 1]},
+#             gauge={
+#                 'axis': {'range': [0, 100]},
+#                 'bar': {'color': "#667eea"},
+#                 'steps': [
+#                     {'range': [0, 35], 'color': "rgba(217, 83, 79, 0.2)"},
+#                     {'range': [35, 65], 'color': "rgba(240, 173, 78, 0.2)"},
+#                     {'range': [65, 100], 'color': "rgba(92, 184, 92, 0.2)"}
+#                 ],
+#             }
+#         ))
+#         gauge_fig.update_layout(height=200, margin=dict(l=30, r=30, t=50, b=20))
+#         gauge_content = dcc.Graph(figure=gauge_fig)
+#     else:
+#         # 處理無法計算分數的情況
+#         error_fig = go.Figure().add_annotation(text="今日尚無情緒分數", showarrow=False)
+#         error_fig.update_layout(height=200)
+#         gauge_content = dcc.Graph(figure=error_fig)
+
+
+#     # 2. 從 predictor 獲取分數最高的3則新聞
+#     top_news_list = predictor.get_news()
+
+#     # 3. 建立新聞摘要元件
+#     if top_news_list: # 如果列表不為空
+#         news_content = html.Div([
+#             html.P(f"• {news}", style={
+#                 'margin': '8px 0',
+#                 'padding-left': '5px',
+#                 'font-size': '14px',
+#                 'border-left': '3px solid #E74C3C'
+#             }) for news in top_news_list
+#         ])
+#     else:
+#         news_content = html.Div("今日尚無重大新聞摘要。")
+    
+#     return gauge_content, news_content
 
 
 @app.callback(